/AutoRCCar

[hamuchiwa/AutoRCCar]项目的中文文档。每个代码文件都有同名markdwon文件来讲解。这是一个用 RC 小车、树莓派、Arduino和开源软件实现的小规模的自动驾驶项目。

Primary LanguagePythonBSD 2-Clause "Simplified" LicenseBSD-2-Clause

AutoRCCar [中文]

查看运动中的小车（YouTube视频，显示不了实属正常）

这是一个用 RC 小车、树莓派、Arduino和开源软件实现的小规模的自动驾驶项目。 系统使用了Raspberry Pi带着一个摄像头和一个用来测距的超声传感器，一个主机操作驾驶方向，物体识别（这里识别的是停止标志和红绿灯）、目标物测距，一个Arduino board用来控制小车

依赖

  • Raspberry Pi:
    • Picamera
  • Computer:
    • Numpy
    • OpenCV 2.4.10.1
    • Pygame
    • PiSerial

目录结构

  • raspberrt_pi
    • stream_client.py：以jpeg格式将视频帧流式传输到主机
    • ultrasonic_client.py：将由传感器测量的距离数据发送到主机
  • Arduino
    • rc_keyboard_control.ino：作为rc控制器和计算机之间的接口，允许用户通过USB串行接口发送命令
  • 电脑
    • cascade_xml 训练级联分类器xml文件
    • 棋盘 用于校准的图像，由pi相机捕获
    • training_data 以npz格式训练神经网络的图像数据
    • testing_data 以npz格式测试神经网络的图像数据
    • training_images 在图像训练数据采集阶段保存视频帧（可选）
    • mlp_xml 在xml文件中训练神经网络参数
    • rc_control_test.py：带键盘的驱动RC车（测试目的）
    • picam_calibration.py：pi相机校准，返回相机矩阵
    • collect_training_data.py：接收流式视频帧和标签框以供后续培训
    • mlp_training.py：神经网络训练
    • mlp_predict_test.py：用测试数据测试训练有素的神经网络
    • rc_driver.py：多线程服务器程序接收视频帧和传感器数据，并允许RC车载驱动器本身具有停车标志，交通灯检测和前碰撞避免能力

如何开车

  1. Flash Arduino：Flash “rc_keyboard_control.ino”到Arduino并运行“rc_control_test.py”来驱动rc车用键盘（测试目的）

  2. Pi相机校准：使用pi相机以各种角度拍摄多张棋盘图像，并将其放入“chess_board”文件夹中，运行“picam_calibration.py”，并返回相机矩阵，这些参数将用于“rc_driver.py”

  3. 收集培训数据和测试数据：首先运行“collect_training_data.py”，然后在raspberry pi上运行“stream_client.py”。用户按键盘驱动RC车，只有当有按键动作时才保存框架。完成驾驶后，按“q”退出，数据保存为npz文件。

  4. 神经网络训练：运行“mlp_training.py”，取决于所选择的参数，需要一些时间训练。培训后，参数保存在“mlp_xml”文件夹中

  5. 神经网络测试：运行“mlp_predict_test.py”从“test_data”文件夹加载测试数据，并从“mlp_xml”文件夹中的xml文件中训练参数

  6. 级联分类器训练（可选）：训练有素的停车标志和交通灯分类器包含在“cascade_xml”文件夹中，如果您有兴趣培训您自己的分类器，请参考OpenCV文档和Thorsten Ball

  7. 自驾驾驶：首先运行“rc_driver.py”在计算机上启动服务器，然后在raspberry pi上运行“stream_client.py”和“ultrasonic_client.py”。

原项目是适用于python 2.7 本项目改成适用于 python3

test 文件夹下讲解不丰富，待改进

AutoRCCar [English]

See self-driving in action

A scaled down version of self-driving system using a RC car, Raspberry Pi, Arduino and open source software. The system uses a Raspberry Pi with a camera and an ultrasonic sensor as inputs, a processing computer that handles steering, object recognition (stop sign and traffic light) and distance measurement, and an Arduino board for RC car control.

Dependencies

  • Raspberry Pi:
    • Picamera
  • Computer:
    • Numpy
    • OpenCV 2.4.10.1
    • Pygame
    • PiSerial

About

  • raspberrt_pi/
    • stream_client.py: stream video frames in jpeg format to the host computer
    • ultrasonic_client.py: send distance data measured by sensor to the host computer
  • arduino/
    • rc_keyboard_control.ino: acts as a interface between rc controller and computer and allows user to send command via USB serial interface
  • computer/
    • cascade_xml/
      • trained cascade classifiers xml files
    • chess_board/
      • images for calibration, captured by pi camera
    • training_data/
      • training data for neural network in npz format
    • training_images/
      • saved video frames during image training data collection stage (optional)
    • mlp_xml/
      • trained neural network parameters in a xml file
    • picam_calibration.py: pi camera calibration, returns camera matrix
    • collect_training_data.py: receive streamed video frames and label frames for later training
    • mlp_training.py: neural network training
    • rc_driver.py: a multithread server program receives video frames and sensor data, and allows RC car drives by itself with stop sign, traffic light detection and front collision avoidance capabilities
  • test/
    • rc_control_test.py: RC car control with keyboard
    • stream_server_test.py: video streaming from Pi to computer
    • ultrasonic_server_test.py: sensor data streaming from Pi to computer
  • Traffic_signal/
    • trafic signal sketch contributed by @geek111

How to drive

  1. Flash Arduino: Flash “rc_keyboard_control.ino” to Arduino and run “rc_control_test.py” to drive the rc car with keyboard (testing purpose)

  2. Pi Camera calibration: Take multiple chess board images using pi camera at various angles and put them into “chess_board” folder, run “picam_calibration.py” and it returns the camera matrix, those parameters will be used in “rc_driver.py”

  3. Collect training data and testing data: First run “collect_training_data.py” and then run “stream_client.py” on raspberry pi. User presses keyboard to drive the RC car, frames are saved only when there is a key press action. When finished driving, press “q” to exit, data is saved as a npz file.

  4. Neural network training: Run “mlp_training.py”, depend on the parameters chosen, it will take some time to train. After training, model will be saved in “mlp_xml” folder

  5. Cascade classifiers training (optional): trained stop sign and traffic light classifiers are included in the "cascade_xml" folder, if you are interested in training your own classifiers, please refer to OpenCV documentation and this great tutorial by Thorsten Ball

  6. Self-driving in action: First run “rc_driver.py” to start the server on the computer and then run “stream_client.py” and “ultrasonic_client.py” on raspberry pi.